Overview

Dataset statistics

Number of variables19
Number of observations2928
Missing cells0
Missing cells (%)0.0%
Total size in memory434.8 KiB
Average record size in memory152.0 B

Variable types

Categorical2
Numeric17

Dataset

DescriptionThe life expectancy data-set contains health factors for 193 countries for the years 2000-2015 has been collected from a WHO data repository website and its corresponding economic data was collected from United Nation website. The dataset consists of 22 Columns and 2938 rows which meant 20 predicting variables
Creatorunknown
URLhttps://www.kaggle.com/datasets/kumarajarshi/life-expectancy-who
Copyright(c) unknown

Alerts

Country has a high cardinality: 183 distinct valuesHigh cardinality
Status is highly overall correlated with LifeExpectancy and 4 other fieldsHigh correlation
LifeExpectancy is highly overall correlated with Status and 9 other fieldsHigh correlation
AdultMort is highly overall correlated with LifeExpectancy and 3 other fieldsHigh correlation
InfD is highly overall correlated with Measles and 3 other fieldsHigh correlation
EtOH is highly overall correlated with Status and 4 other fieldsHigh correlation
PercExpen is highly overall correlated with StatusHigh correlation
Measles is highly overall correlated with InfD and 1 other fieldsHigh correlation
BMI is highly overall correlated with LifeExpectancy and 4 other fieldsHigh correlation
lt5yD is highly overall correlated with InfD and 3 other fieldsHigh correlation
Polio is highly overall correlated with LifeExpectancy and 1 other fieldsHigh correlation
DTP is highly overall correlated with LifeExpectancy and 1 other fieldsHigh correlation
HIV is highly overall correlated with LifeExpectancy and 1 other fieldsHigh correlation
Thin1_19y is highly overall correlated with LifeExpectancy and 7 other fieldsHigh correlation
Thin5_9y is highly overall correlated with LifeExpectancy and 6 other fieldsHigh correlation
Income is highly overall correlated with Status and 6 other fieldsHigh correlation
Education is highly overall correlated with Status and 7 other fieldsHigh correlation
Country is uniformly distributedUniform
InfD has 838 (28.6%) zerosZeros
PercExpen has 606 (20.7%) zerosZeros
Measles has 973 (33.2%) zerosZeros
lt5yD has 775 (26.5%) zerosZeros
Income has 130 (4.4%) zerosZeros

Reproduction

Analysis started2023-02-03 14:44:18.172963
Analysis finished2023-02-03 14:44:46.590497
Duration28.42 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

Country
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct183
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
Afghanistan
 
16
New Zealand
 
16
Niger
 
16
Nigeria
 
16
Norway
 
16
Other values (178)
2848 

Length

Max length52
Median length34
Mean length10.04371585
Min length4

Characters and Unicode

Total characters29408
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan

Common Values

ValueCountFrequency (%)
Afghanistan 16
 
0.5%
New Zealand 16
 
0.5%
Niger 16
 
0.5%
Other values (180) 2880
98.4%

Length

2023-02-03T09:44:46.662314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of 192
 
4.5%
republic 192
 
4.5%
and 96
 
2.2%
united 64
 
1.5%
democratic 48
 
1.1%
guinea 48
 
1.1%
the 48
 
1.1%
people's 32
 
0.7%
states 32
 
0.7%
arab 32
 
0.7%
Other values (211) 3488
81.6%

Most occurring characters

ValueCountFrequency (%)
a 4176
 
14.2%
i 2528
 
8.6%
e 2176
 
7.4%
Other values (53) 20528
69.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23904
81.3%
Uppercase Letter 3952
 
13.4%
Space Separator 1344
 
4.6%
Other values (4) 208
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4176
 
17.5%
i 2528
 
10.6%
e 2176
 
9.1%
Other values (24) 15024
62.9%
Uppercase Letter
ValueCountFrequency (%)
S 464
 
11.7%
B 336
 
8.5%
C 288
 
7.3%
Other values (21) 2864
72.5%
Space Separator
ValueCountFrequency (%)
1344
100.0%
Open Punctuation
ValueCountFrequency (%)
( 64
100.0%
Close Punctuation
ValueCountFrequency (%)
) 64
100.0%
Other Punctuation
ValueCountFrequency (%)
' 48
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27856
94.7%
Common 1552
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4176
 
15.0%
i 2528
 
9.1%
e 2176
 
7.8%
Other values (48) 18976
68.1%
Common
ValueCountFrequency (%)
1344
86.6%
( 64
 
4.1%
) 64
 
4.1%
Other values (2) 80
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29392
99.9%
None 16
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4176
 
14.2%
i 2528
 
8.6%
e 2176
 
7.4%
Other values (52) 20512
69.8%
None
ValueCountFrequency (%)
ô 16
100.0%

Year
Real number (ℝ)

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.5
Minimum2000
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2023-02-03T09:44:46.755244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2000
Q12003.75
median2007.5
Q32011.25
95-th percentile2015
Maximum2015
Range15
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation4.610559618
Coefficient of variation (CV)0.002296667307
Kurtosis-1.209427576
Mean2007.5
Median Absolute Deviation (MAD)4
Skewness0
Sum5877960
Variance21.25725999
MonotonicityNot monotonic
2023-02-03T09:44:46.842050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2015 183
 
6.2%
2014 183
 
6.2%
2013 183
 
6.2%
Other values (13) 2379
81.2%
ValueCountFrequency (%)
2000 183
6.2%
2001 183
6.2%
2002 183
6.2%
ValueCountFrequency (%)
2015 183
6.2%
2014 183
6.2%
2013 183
6.2%

Status
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
0
2416 
1
512 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2928
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2416
82.5%
1 512
 
17.5%

Length

2023-02-03T09:44:46.935492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-03T09:44:47.025673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2416
82.5%
1 512
 
17.5%

Most occurring characters

ValueCountFrequency (%)
0 2416
82.5%
1 512
 
17.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2928
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2416
82.5%
1 512
 
17.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2928
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2416
82.5%
1 512
 
17.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2928
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2416
82.5%
1 512
 
17.5%

LifeExpectancy
Real number (ℝ)

Distinct362
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.22493169
Minimum36.3
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2023-02-03T09:44:47.342883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum36.3
5-th percentile51.4
Q163.1
median72.1
Q375.7
95-th percentile82
Maximum89
Range52.7
Interquartile range (IQR)12.6

Descriptive statistics

Standard deviation9.523867488
Coefficient of variation (CV)0.1375785754
Kurtosis-0.2344773942
Mean69.22493169
Median Absolute Deviation (MAD)5.8
Skewness-0.6386047359
Sum202690.6
Variance90.70405193
MonotonicityNot monotonic
2023-02-03T09:44:47.455943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 45
 
1.5%
75 33
 
1.1%
78 31
 
1.1%
Other values (359) 2819
96.3%
ValueCountFrequency (%)
36.3 1
< 0.1%
39 1
< 0.1%
41 1
< 0.1%
ValueCountFrequency (%)
89 11
0.4%
88 10
0.3%
87 9
0.3%

AdultMort
Real number (ℝ)

Distinct425
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.7964481
Minimum1
Maximum723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2023-02-03T09:44:47.567357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q174
median144
Q3228
95-th percentile398.3
Maximum723
Range722
Interquartile range (IQR)154

Descriptive statistics

Standard deviation124.292079
Coefficient of variation (CV)0.754215764
Kurtosis1.748860208
Mean164.7964481
Median Absolute Deviation (MAD)76
Skewness1.174369488
Sum482524
Variance15448.5209
MonotonicityNot monotonic
2023-02-03T09:44:47.673977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 34
 
1.2%
14 30
 
1.0%
16 29
 
1.0%
Other values (422) 2835
96.8%
ValueCountFrequency (%)
1 12
0.4%
2 8
0.3%
3 6
0.2%
ValueCountFrequency (%)
723 1
< 0.1%
717 1
< 0.1%
715 1
< 0.1%

InfD
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct209
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.40744536
Minimum0
Maximum1800
Zeros838
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2023-02-03T09:44:47.789096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q322
95-th percentile94.65
Maximum1800
Range1800
Interquartile range (IQR)22

Descriptive statistics

Standard deviation118.1144496
Coefficient of variation (CV)3.884392399
Kurtosis115.6574795
Mean30.40744536
Median Absolute Deviation (MAD)3
Skewness9.771044493
Sum89033
Variance13951.0232
MonotonicityNot monotonic
2023-02-03T09:44:47.896746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 838
28.6%
1 342
 
11.7%
2 203
 
6.9%
Other values (206) 1545
52.8%
ValueCountFrequency (%)
0 838
28.6%
1 342
11.7%
2 203
 
6.9%
ValueCountFrequency (%)
1800 2
0.1%
1700 2
0.1%
1600 1
< 0.1%

EtOH
Real number (ℝ)

Distinct1077
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.614855576
Minimum0.01
Maximum17.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2023-02-03T09:44:48.013632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q11.1075
median4.16
Q37.4
95-th percentile11.89
Maximum17.87
Range17.86
Interquartile range (IQR)6.2925

Descriptive statistics

Standard deviation3.914923272
Coefficient of variation (CV)0.8483306158
Kurtosis-0.6491103361
Mean4.614855576
Median Absolute Deviation (MAD)3.14
Skewness0.6076218711
Sum13512.29713
Variance15.32662423
MonotonicityNot monotonic
2023-02-03T09:44:48.126484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 280
 
9.6%
4.614855576 193
 
6.6%
0.03 15
 
0.5%
Other values (1074) 2440
83.3%
ValueCountFrequency (%)
0.01 280
9.6%
0.02 12
 
0.4%
0.03 15
 
0.5%
ValueCountFrequency (%)
17.87 1
< 0.1%
17.31 1
< 0.1%
16.99 1
< 0.1%

PercExpen
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2323
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean740.321185
Minimum0
Maximum19479.91161
Zeros606
Zeros (%)20.7%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2023-02-03T09:44:48.251463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.853963995
median65.61145482
Q3442.6143215
95-th percentile4507.913607
Maximum19479.91161
Range19479.91161
Interquartile range (IQR)437.7603575

Descriptive statistics

Standard deviation1990.930605
Coefficient of variation (CV)2.689279525
Kurtosis26.47582908
Mean740.321185
Median Absolute Deviation (MAD)65.61145482
Skewness4.643789672
Sum2167660.43
Variance3963804.673
MonotonicityNot monotonic
2023-02-03T09:44:48.371908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 606
 
20.7%
71.27962362 1
 
< 0.1%
15.25518816 1
 
< 0.1%
Other values (2320) 2320
79.2%
ValueCountFrequency (%)
0 606
20.7%
0.09987219 1
 
< 0.1%
0.108055973 1
 
< 0.1%
ValueCountFrequency (%)
19479.91161 1
< 0.1%
19099.04506 1
< 0.1%
18961.3486 1
< 0.1%

Measles
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct958
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2427.855874
Minimum0
Maximum212183
Zeros973
Zeros (%)33.2%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2023-02-03T09:44:48.500119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17
Q3362.25
95-th percentile9994.05
Maximum212183
Range212183
Interquartile range (IQR)362.25

Descriptive statistics

Standard deviation11485.97094
Coefficient of variation (CV)4.73091136
Kurtosis114.4679785
Mean2427.855874
Median Absolute Deviation (MAD)17
Skewness9.425290043
Sum7108762
Variance131927528.4
MonotonicityNot monotonic
2023-02-03T09:44:48.613308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 973
33.2%
1 104
 
3.6%
2 68
 
2.3%
Other values (955) 1783
60.9%
ValueCountFrequency (%)
0 973
33.2%
1 104
 
3.6%
2 68
 
2.3%
ValueCountFrequency (%)
212183 1
< 0.1%
182485 1
< 0.1%
168107 1
< 0.1%

BMI
Real number (ℝ)

Distinct603
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.23539365
Minimum1
Maximum77.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2023-02-03T09:44:48.729619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.2
Q119.4
median43
Q356.1
95-th percentile64.5
Maximum77.6
Range76.6
Interquartile range (IQR)36.7

Descriptive statistics

Standard deviation19.8501842
Coefficient of variation (CV)0.5191573123
Kurtosis-1.294361545
Mean38.23539365
Median Absolute Deviation (MAD)16.3
Skewness-0.2318276989
Sum111953.2326
Variance394.0298128
MonotonicityNot monotonic
2023-02-03T09:44:48.846822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.23539365 32
 
1.1%
58.5 18
 
0.6%
57 16
 
0.5%
Other values (600) 2862
97.7%
ValueCountFrequency (%)
1 1
< 0.1%
1.4 2
0.1%
1.8 1
< 0.1%
ValueCountFrequency (%)
77.6 1
< 0.1%
77.1 1
< 0.1%
76.7 1
< 0.1%

lt5yD
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct252
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.17930328
Minimum0
Maximum2500
Zeros775
Zeros (%)26.5%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2023-02-03T09:44:48.965120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q328
95-th percentile138
Maximum2500
Range2500
Interquartile range (IQR)28

Descriptive statistics

Standard deviation160.7005471
Coefficient of variation (CV)3.809938395
Kurtosis109.3884348
Mean42.17930328
Median Absolute Deviation (MAD)4
Skewness9.479622923
Sum123501
Variance25824.66582
MonotonicityNot monotonic
2023-02-03T09:44:49.079512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 775
26.5%
1 361
 
12.3%
2 163
 
5.6%
Other values (249) 1629
55.6%
ValueCountFrequency (%)
0 775
26.5%
1 361
12.3%
2 163
 
5.6%
ValueCountFrequency (%)
2500 1
< 0.1%
2400 1
< 0.1%
2300 1
< 0.1%

Polio
Real number (ℝ)

Distinct74
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.54829838
Minimum3
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2023-02-03T09:44:49.194816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range96
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.34054762
Coefficient of variation (CV)0.2827501968
Kurtosis3.822675769
Mean82.54829838
Median Absolute Deviation (MAD)6
Skewness-2.10479121
Sum241701.4177
Variance544.781163
MonotonicityNot monotonic
2023-02-03T09:44:49.307852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 373
 
12.7%
98 254
 
8.7%
96 205
 
7.0%
Other values (71) 2096
71.6%
ValueCountFrequency (%)
3 7
0.2%
4 11
0.4%
5 8
0.3%
ValueCountFrequency (%)
99 373
12.7%
98 254
8.7%
97 205
7.0%

TotalExpen
Real number (ℝ)

Distinct817
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.930162842
Minimum0.37
Maximum17.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2023-02-03T09:44:49.421450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.37
5-th percentile1.9835
Q14.37
median5.930162842
Q37.33
95-th percentile9.6865
Maximum17.6
Range17.23
Interquartile range (IQR)2.96

Descriptive statistics

Standard deviation2.38547781
Coefficient of variation (CV)0.4022617715
Kurtosis1.32780271
Mean5.930162842
Median Absolute Deviation (MAD)1.499837158
Skewness0.6008632384
Sum17363.5168
Variance5.690504383
MonotonicityNot monotonic
2023-02-03T09:44:49.532979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.930162842 226
 
7.7%
4.6 15
 
0.5%
6.7 12
 
0.4%
Other values (814) 2675
91.4%
ValueCountFrequency (%)
0.37 1
< 0.1%
0.65 1
< 0.1%
0.74 1
< 0.1%
ValueCountFrequency (%)
17.6 1
< 0.1%
17.2 2
0.1%
17.14 1
< 0.1%

DTP
Real number (ℝ)

Distinct82
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.32141629
Minimum2
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2023-02-03T09:44:49.654576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range97
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.62957557
Coefficient of variation (CV)0.2870404402
Kurtosis3.602063
Mean82.32141629
Median Absolute Deviation (MAD)6
Skewness-2.079351622
Sum241037.1069
Variance558.3568417
MonotonicityNot monotonic
2023-02-03T09:44:49.770301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 347
 
11.9%
98 253
 
8.6%
97 205
 
7.0%
Other values (79) 2123
72.5%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 4
 
0.1%
4 12
0.4%
ValueCountFrequency (%)
99 347
11.9%
98 253
8.6%
97 205
7.0%

HIV
Real number (ℝ)

Distinct200
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.747711749
Minimum0.1
Maximum50.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2023-02-03T09:44:49.881751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.1
median0.1
Q30.8
95-th percentile8.565
Maximum50.6
Range50.5
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation5.08554241
Coefficient of variation (CV)2.909829046
Kurtosis34.76639831
Mean1.747711749
Median Absolute Deviation (MAD)0
Skewness5.386623166
Sum5117.3
Variance25.8627416
MonotonicityNot monotonic
2023-02-03T09:44:49.994965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 1771
60.5%
0.2 124
 
4.2%
0.3 115
 
3.9%
Other values (197) 918
31.4%
ValueCountFrequency (%)
0.1 1771
60.5%
0.2 124
 
4.2%
0.3 115
 
3.9%
ValueCountFrequency (%)
50.6 1
< 0.1%
50.3 1
< 0.1%
49.9 1
< 0.1%

Thin1_19y
Real number (ℝ)

Distinct201
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.850621547
Minimum0.1
Maximum27.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2023-02-03T09:44:50.104071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q11.6
median3.4
Q37.1
95-th percentile13.8
Maximum27.7
Range27.6
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation4.396596926
Coefficient of variation (CV)0.9063986715
Kurtosis4.042951627
Mean4.850621547
Median Absolute Deviation (MAD)2.35
Skewness1.720041277
Sum14202.61989
Variance19.33006453
MonotonicityNot monotonic
2023-02-03T09:44:50.209861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 74
 
2.5%
1.9 65
 
2.2%
0.8 64
 
2.2%
Other values (198) 2725
93.1%
ValueCountFrequency (%)
0.1 23
0.8%
0.2 39
1.3%
0.3 32
1.1%
ValueCountFrequency (%)
27.7 1
< 0.1%
27.5 1
< 0.1%
27.4 1
< 0.1%

Thin5_9y
Real number (ℝ)

Distinct208
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.881422652
Minimum0.1
Maximum28.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2023-02-03T09:44:50.324904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11.6
median3.4
Q37.2
95-th percentile13.8
Maximum28.6
Range28.5
Interquartile range (IQR)5.6

Descriptive statistics

Standard deviation4.48489019
Coefficient of variation (CV)0.9187670297
Kurtosis4.435351724
Mean4.881422652
Median Absolute Deviation (MAD)2.4
Skewness1.786369073
Sum14292.80552
Variance20.11424002
MonotonicityNot monotonic
2023-02-03T09:44:50.433305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9 69
 
2.4%
1.1 67
 
2.3%
0.5 63
 
2.2%
Other values (205) 2729
93.2%
ValueCountFrequency (%)
0.1 31
1.1%
0.2 45
1.5%
0.3 25
0.9%
ValueCountFrequency (%)
28.6 1
< 0.1%
28.5 1
< 0.1%
28.4 1
< 0.1%

Income
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct626
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6274187139
Minimum0
Maximum0.948
Zeros130
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2023-02-03T09:44:50.549251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.291
Q10.504
median0.662
Q30.773
95-th percentile0.89
Maximum0.948
Range0.948
Interquartile range (IQR)0.269

Descriptive statistics

Standard deviation0.2051306297
Coefficient of variation (CV)0.3269437541
Kurtosis1.641921102
Mean0.6274187139
Median Absolute Deviation (MAD)0.125
Skewness-1.174653309
Sum1837.081994
Variance0.04207857523
MonotonicityNot monotonic
2023-02-03T09:44:50.661920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6274187139 160
 
5.5%
0 130
 
4.4%
0.7 17
 
0.6%
Other values (623) 2621
89.5%
ValueCountFrequency (%)
0 130
4.4%
0.253 1
 
< 0.1%
0.255 1
 
< 0.1%
ValueCountFrequency (%)
0.948 1
< 0.1%
0.945 1
< 0.1%
0.942 1
< 0.1%

Education
Real number (ℝ)

Distinct174
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.99963873
Minimum0
Maximum20.7
Zeros26
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2023-02-03T09:44:50.781945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110.3
median12.1
Q314.1
95-th percentile16.8
Maximum20.7
Range20.7
Interquartile range (IQR)3.8

Descriptive statistics

Standard deviation3.253690955
Coefficient of variation (CV)0.2711490761
Kurtosis1.061215606
Mean11.99963873
Median Absolute Deviation (MAD)1.9
Skewness-0.6005046803
Sum35134.9422
Variance10.58650483
MonotonicityNot monotonic
2023-02-03T09:44:50.895542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.99963873 160
 
5.5%
12.9 58
 
2.0%
13.3 52
 
1.8%
Other values (171) 2658
90.8%
ValueCountFrequency (%)
0 26
0.9%
2.8 1
 
< 0.1%
2.9 4
 
0.1%
ValueCountFrequency (%)
20.7 1
< 0.1%
20.6 1
< 0.1%
20.5 1
< 0.1%

Interactions

2023-02-03T09:44:44.658144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:18.577542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:20.199331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:21.814566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:23.413318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:25.101726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:26.678032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:28.416497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:29.921907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:31.595594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:33.308144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:34.874299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:36.494458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:37.995709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:39.709347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:41.392748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:43.122254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:44.750552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:18.676575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:20.298432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:21.913402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:23.511788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:25.200961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:26.779486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:28.508002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:30.025269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:31.691001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:33.404204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:34.983154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:36.595292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:38.085178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:39.815848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:41.502829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:43.220879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:44.838733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:18.771292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:20.387611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:22.006230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:23.602940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:25.292399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:26.871746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:28.592932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:30.121408image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:31.778583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:33.491617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:35.078466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:36.686832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:38.168531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:39.912313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:41.602724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:43.307469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:44.932128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:18.871902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:20.483593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:22.102266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:23.699051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:25.388414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:26.970705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:28.686700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:30.225793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:31.871139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:33.599447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:35.186512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:36.783952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:38.256693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:40.013469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:41.709896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:43.399642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:45.024078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:18.971011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:20.577348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:22.198417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:23.794876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:25.484426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:27.068221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:28.778171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:30.330432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:31.963382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:33.694698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:35.288661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:36.880072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:38.353163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:40.115763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:41.819717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:43.488997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:45.117725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:19.071005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:20.751480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:22.296571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:23.889985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:25.580189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:27.167513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:28.872344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:30.429974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:32.055775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:33.789256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:35.385318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:36.973552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:38.451716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:40.219233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:41.929491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:43.578981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:45.219078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:19.175028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:20.849730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:22.396775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:24.094507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:25.682345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:27.268144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:28.969095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:30.536966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:32.152788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:33.888915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:35.484087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:37.068088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:38.553656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:40.325465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:42.041590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:43.681893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:45.305334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:19.261365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:20.935659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:22.485176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:24.183443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:25.768167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:27.358320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:29.049755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:30.645592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:32.235355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:33.976270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:35.568367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:37.148378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:38.640698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:40.417234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:42.136241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:43.770424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:45.404319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:19.363661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:21.031155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:22.587646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:24.284611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:25.868680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:27.459919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:29.149249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:30.747373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:32.334878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:34.074261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:35.665210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:37.243761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:38.738481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:40.521770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:42.242477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:43.872016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:45.490302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:19.455117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:21.117817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:22.678522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:24.375983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:25.957882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:27.552305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:29.234280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:30.843086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:32.425585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:34.162502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:35.752615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:37.327655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:38.823135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:40.616928image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:42.338131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:43.961481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:45.575544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:19.545878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:21.204354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:22.770172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:24.465996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:26.048292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:27.645232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:29.319328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:30.938113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:32.520164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:34.250141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:35.842060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:37.410394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:38.906382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:40.713540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:42.435083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:44.049245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:45.671955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:19.644350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:21.299228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:22.869984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:24.562979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:26.143115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:27.744350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:29.411940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:31.043175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:32.620235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:34.346587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:35.936199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:37.501667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:38.996488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:40.823621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:42.544557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:44.143078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:45.760295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:19.736560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:21.386100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:22.961773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:24.653936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:26.233284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:27.956373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:29.498164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:31.140232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:32.707779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:34.433788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:36.022828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:37.583569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:39.260903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:40.919950image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:42.647783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:44.231059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:45.849483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:19.829458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:21.472275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:23.054099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:24.743793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:26.323215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:28.050341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:29.582017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:31.235143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:32.799927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:34.521486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:36.115940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:37.666804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:39.344340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:41.014141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:42.748378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:44.319831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:45.933634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:19.921993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:21.559663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:23.144871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:24.833678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:26.412824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:28.143255image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:29.667316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:31.326629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:33.045716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:34.609720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:36.212628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:37.749689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:39.429492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:41.108645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:42.848702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:44.407538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:46.021080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:20.018860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:21.649692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:23.239345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:24.927253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:26.505491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:28.239645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:29.755983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:31.419662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:33.136325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:34.704195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:36.307247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:37.836832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:39.526403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:41.205883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:42.946728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:44.497250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:46.101426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:20.108722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:21.732728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:23.326295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:25.015350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:26.593022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:28.328174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:29.839040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:31.507844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:33.223664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:34.789675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:36.400723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:37.917031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:39.615252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:41.298868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:43.032634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T09:44:44.578052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-02-03T09:44:51.002171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
YearStatusLifeExpectancyAdultMortInfDEtOHPercExpenMeaslesBMIlt5yDPolioTotalExpenDTPHIVThin1_19yThin5_9yIncomeEducation
Year1.0000.0000.170-0.079-0.036-0.0450.033-0.0820.104-0.0420.0940.0790.134-0.139-0.045-0.0480.2360.207
Status0.0001.0000.482-0.315-0.1130.5790.454-0.0770.315-0.1160.2210.2940.217-0.149-0.370-0.3680.4580.493
LifeExpectancy0.1700.4821.000-0.696-0.1970.3920.382-0.1580.562-0.2230.4630.2100.476-0.557-0.473-0.4670.6930.719
AdultMort-0.079-0.315-0.6961.0000.079-0.191-0.2430.031-0.3840.094-0.273-0.112-0.2740.5240.3000.306-0.440-0.437
InfD-0.036-0.113-0.1970.0791.000-0.115-0.0860.501-0.2270.997-0.171-0.127-0.1760.0250.4660.471-0.144-0.193
EtOH-0.0450.5790.392-0.191-0.1151.0000.339-0.0520.326-0.1120.2140.3010.216-0.050-0.421-0.4090.4170.498
PercExpen0.0330.4540.382-0.243-0.0860.3391.000-0.0570.231-0.0880.1480.1750.144-0.098-0.252-0.2540.3810.390
Measles-0.082-0.077-0.1580.0310.501-0.052-0.0571.000-0.1760.508-0.136-0.105-0.1420.0310.2250.221-0.116-0.124
BMI0.1040.3150.562-0.384-0.2270.3260.231-0.1761.000-0.2380.2850.2280.284-0.244-0.531-0.5380.4820.519
lt5yD-0.042-0.116-0.2230.0940.997-0.112-0.0880.508-0.2381.000-0.189-0.128-0.1960.0380.4680.472-0.161-0.209
Polio0.0940.2210.463-0.273-0.1710.2140.148-0.1360.285-0.1891.0000.1370.672-0.160-0.221-0.2220.3560.384
TotalExpen0.0790.2940.210-0.112-0.1270.3010.175-0.1050.228-0.1280.1371.0000.152-0.000-0.268-0.2740.1500.232
DTP0.1340.2170.476-0.274-0.1760.2160.144-0.1420.284-0.1960.6720.1521.000-0.165-0.229-0.2220.3720.389
HIV-0.139-0.149-0.5570.5240.025-0.050-0.0980.031-0.2440.038-0.160-0.000-0.1651.0000.2030.207-0.247-0.220
Thin1_19y-0.045-0.370-0.4730.3000.466-0.421-0.2520.225-0.5310.468-0.221-0.268-0.2290.2031.0000.939-0.407-0.452
Thin5_9y-0.048-0.368-0.4670.3060.471-0.409-0.2540.221-0.5380.472-0.222-0.274-0.2220.2070.9391.000-0.396-0.441
Income0.2360.4580.693-0.440-0.1440.4170.381-0.1160.482-0.1610.3560.1500.372-0.247-0.407-0.3961.0000.800
Education0.2070.4930.719-0.437-0.1930.4980.390-0.1240.519-0.2090.3840.2320.389-0.220-0.452-0.4410.8001.000

Missing values

2023-02-03T09:44:46.243065image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-03T09:44:46.501405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.